Computerized system and method for automatically extracting GIFs from videos

ABSTRACT

Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically extracting and creating an animated Graphics Interchange Format (GIF) file from a media file. The disclosed systems and methods identify a number of GIF candidates from a video file, and based on analysis of each candidate&#39;s attributes, features and/or qualities, as well as determinations related to an optimal playback setting for the content of each GIF candidate, at least one GIF candidate is automatically provided to a user for rendering.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims priority from U.S.patent application Ser. No. 14/933,397, filed on Nov. 5, 2015 (now U.S.Pat. No. 9,799,373), which is incorporated herein by reference in itsentirety.

This application includes material that is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD

The present disclosure relates generally to improving the performance ofcontent generating, searching, providing and/or hosting computer systemsand/or platforms by modifying the capabilities and providing non-nativefunctionality to such systems and/or platforms for automaticallyextracting and creating an animated GIF from a video file.

SUMMARY

The present disclosure provides novel systems and methods for automaticextraction and creation of animated Graphics Interchange Format (GIF)files from a video file. GIFs are efficient at displaying moving imagesin lieu of actual video files. They are compressed and low bandwidth andrequire no complex video editing tools.

Presently, GIFs are used in a wide variety of network applications, suchas social network sites, blogs, news, and other content distributionservices. While becoming more prevalent, the creation of GIFs remains amanual and labor intensive process. The automatic GIF creation describedherein enables rapid and automatic GIF development from video contentwith a high likelihood that the automatically created GIF will attainhigh visibility when shared in a network. This presents improvements tothe quality of, and distribution of, user generated content.

According to some embodiments, the disclosed systems and methods firstidentifies a number of “GIF candidates” (also referred to as “shots”,and understood as segments or portions) of a video file by determiningshot boundaries within the video. Such shot boundaries, which delineatethe segments of the video file, are associated with transition frameswithin the video file. For example, such transition frames can include,but are not limited to, a cut between video frames, fade in/out betweenframes, dissolve or wipe effect(s), and/or any other type of known or tobe known effect that transitions between scenes of content within avideo file.

The disclosed systems and methods then evaluate each GIF candidate interms of “GIF quality” metrics thereby resulting in a score for each GIFcandidate. “GIF quality” metrics can be associated with a determinationof the GIF candidate's attributes and/or features, which include, butare not limited to, visual aesthetics, popularity, virality,memorability, sentiment, temporal nature of the content of the GIF,motion occurring within the segment as it is played, induced emotion asthe segment is played, interestingness of the content of the segment,and the like. For example, the higher quality the score, the higher theprobability the animated GIF candidate is trending, or will becometrending, for example, on social media. While evaluating a GIFcandidate, the disclosed systems and methods also determine an optimalplayback speed for each GIF candidate—i.e., frame sampling rate and thetime interval between each frame.

Thus, based on the determinations of the GIF candidates' “GIF quality”and optimal playback speed, the disclosed systems and methods canautomatically create a GIF(s) from a video file that can be rendered atits optimal playback speed. In some embodiments, a selection may alsooccur whereby the GIF candidate having the highest “GIF quality” can beselected for presentation to a user. In some embodiments, only those GIFcandidates having a “GIF quality” satisfying a threshold can bepresented to a user, whereby these candidates can then be selected by auser for rendering, posting and/or sharing.

It will be recognized from the disclosure herein that embodiments of theinstant disclosure provide improvements to a number of technology areas,for example those related to systems and processes that handle orprocess content generation and delivery to users over the internet, suchas but not limited to, search engines, local and/or web-basedapplications, TV widgets, set-top boxes, or other types of mediarendering or recommendation platforms, electronic social networkingplatforms and the like. The disclosed systems and methods can effectuateincreased speed and efficiency in the ways that users can access andcreate new media content, thereby minimizing user effort, as thedisclosed systems and methods, inter alia, reduce the amount of requiredinput for a user that is searching for and/or creating media. Users areprovided with a fully automated experience through the disclosedsystems' and methods' creation and delivery of GIF files generated frommedia files. For example, the disclosed GIF creation and delivery avoidsusers having to manually identify specific portions of video files, asthe disclosed systems and methods can automatically identify the“trending” (or popular) portions of videos and automatically generateGIF files for such portions at optimal playback speeds, thus improvingopportunities for wide distribution and recognition for the GIFassociated with the user.

In accordance with one or more embodiments, a method is disclosed whichincludes receiving, at a computing device, a request from a user forcreation of an animated Graphics Interchange Format (GIF) file from avideo file; determining, via the computing device, a segment within thevideo file, the segment determination comprising parsing the video fileto identify transition frames within the video file, the segmentcomprising video frames of the video file existing between a pair ofidentified transition frames; determining, via the computing device,playback settings for the segment, each playback setting comprising adifferent frame sampling rate and time interval between each frame ofthe segment; determining, via the computing device, an n-dimensionalfeature vector for the segment, the feature vector determinationcomprising parsing the segment to identify information associated withmotion, emotion and interestingness features of the segment, the featurevector based on the motion, emotion and interestingness information;determining, via the computing device, a popularity score for eachplayback setting based on the feature vector, the popularity scoredetermination comprising determining an optimal playback setting for thesegment from the playback settings; and automatically creating, via thecomputing device, the animated GIF file, the animated GIF filecomprising the segment renderable at the optimal playback setting.

In accordance with one or more embodiments, a non-transitorycomputer-readable storage medium is provided, the non-transitorycomputer-readable storage medium tangibly storing thereon, or havingtangibly encoded thereon, computer readable instructions that whenexecuted cause at least one processor to perform a method forautomatically extracting and creating animated GIFs from a video file.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code (or program logic) executed by aprocessor(s) of a computing device to implement functionality inaccordance with one or more such embodiments is embodied in, by and/oron a non-transitory computer-readable medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosure will be apparent from the following description ofembodiments as illustrated in the accompanying drawings, in whichreference characters refer to the same parts throughout the variousviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating principles of the disclosure:

FIG. 1 is a schematic diagram illustrating an example of a networkwithin which the systems and methods disclosed herein could beimplemented according to some embodiments of the present disclosure;

FIG. 2 depicts is a schematic diagram illustrating an example of clientdevice in accordance with some embodiments of the present disclosure;

FIG. 3 is a schematic block diagram illustrating components of anexemplary system in accordance with embodiments of the presentdisclosure;

FIG. 4 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure;

FIG. 5 is a diagram of an exemplary example of a non-limiting embodimentin accordance with some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure; and

FIG. 7 is a block diagram illustrating the architecture of an exemplaryhardware device in accordance with one or more embodiments of thepresent disclosure.

DESCRIPTION OF EMBODIMENTS

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, certain example embodiments. Subjectmatter may, however, be embodied in a variety of different forms and,therefore, covered or claimed subject matter is intended to be construedas not being limited to any example embodiments set forth herein;example embodiments are provided merely to be illustrative. Likewise, areasonably broad scope for claimed or covered subject matter isintended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

These computer program instructions can be provided to a processor of: ageneral purpose computer to alter its function to a special purpose; aspecial purpose computer; ASIC; or other programmable digital dataprocessing apparatus, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, implement the functions/acts specified in the block diagramsor operational block or blocks, thereby transforming their functionalityin accordance with embodiments herein.

For the purposes of this disclosure a computer readable medium (orcomputer-readable storage medium/media) stores computer data, which datacan include computer program code (or computer-executable instructions)that is executable by a computer, in machine readable form. By way ofexample, and not limitation, a computer readable medium may comprisecomputer readable storage media, for tangible or fixed storage of data,or communication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Servers may vary widely inconfiguration or capabilities, but generally a server may include one ormore central processing units and memory. A server may also include oneor more mass storage devices, one or more power supplies, one or morewired or wireless network interfaces, one or more input/outputinterfaces, or one or more operating systems, such as Windows Server,Mac OS X, Unix, Linux, FreeBSD, or the like.

For the purposes of this disclosure a “network” should be understood torefer to a network that may couple devices so that communications may beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), or otherforms of computer or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, cellular or any combination thereof. Likewise,sub-networks, which may employ differing architectures or may becompliant or compatible with differing protocols, may interoperatewithin a larger network. Various types of devices may, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router mayprovide a link between otherwise separate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a wired or wireless lineor link, for example.

For purposes of this disclosure, a “wireless network” should beunderstood to couple client devices with a network. A wireless networkmay employ stand-alone ad-hoc networks, mesh networks, Wireless LAN(WLAN) networks, cellular networks, or the like. A wireless network mayfurther include a system of terminals, gateways, routers, or the likecoupled by wireless radio links, or the like, which may move freely,randomly or organize themselves arbitrarily, such that network topologymay change, at times even rapidly.

A wireless network may further employ a plurality of network accesstechnologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, WirelessRouter (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or 4G)cellular technology, or the like. Network access technologies may enablewide area coverage for devices, such as client devices with varyingdegrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like. Servers may vary widely in configuration or capabilities,but generally a server may include one or more central processing unitsand memory. A server may also include one or more mass storage devices,one or more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like.

For purposes of this disclosure, a client (or consumer or user) devicemay include a computing device capable of sending or receiving signals,such as via a wired or a wireless network. A client device may, forexample, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device an Near Field Communication (NFC)device, a Personal Digital Assistant (PDA), a handheld computer, atablet computer, a phablet, a laptop computer, a set top box, a wearablecomputer, smart watch, an integrated or distributed device combiningvarious features, such as features of the forgoing devices, or the like.

A client device may vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, a simple smart phone, phablet or tablet mayinclude a numeric keypad or a display of limited functionality, such asa monochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device mayinclude a high resolution screen, one or more physical or virtualkeyboards, mass storage, one or more accelerometers, one or moregyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, or a display with a high degree offunctionality, such as a touch-sensitive color 2D or 3D display, forexample.

A client device may include or may execute a variety of operatingsystems, including a personal computer operating system, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like.

A client device may include or may execute a variety of possibleapplications, such as a client software application enablingcommunication with other devices, such as communicating one or moremessages, such as via email, for example Yahoo!® Mail, short messageservice (SMS), or multimedia message service (MMS), for example Yahoo!Messenger®, including via a network, such as a social network,including, for example, Tumblr®, Facebook®, LinkedIn®, Twitter®,Flickr®, or Google+®, Instagram™, to provide only a few possibleexamples. A client device may also include or execute an application tocommunicate content, such as, for example, textual content, multimediacontent, or the like. A client device may also include or execute anapplication to perform a variety of possible tasks, such as browsing,searching, playing or displaying various forms of content, includinglocally stored or streamed video, or games (such as fantasy sportsleagues). The foregoing is provided to illustrate that claimed subjectmatter is intended to include a wide range of possible features orcapabilities.

The principles described herein may be embodied in many different forms.By way of background, an animated Graphics Interchange Format file(known as a GIF), is an image file format encoded with multiple imageframes. Its intended use case is playing an animation of imagescontinuously in an infinite loop. Up until the early 2000's, animatedGIFs have been used primarily for playing simple clipart animations,such as, for example, flames and a waving American flag. However, whensocial networking sites such as Tumblr® and Reddit™ became popular inthe late 2000's, people started using animated GIFs in a more creativeway. Most notably, people started leveraging the large amount of onlinevideos to create animated GIFs, generating numerous famous Internetmemes and Cinemagraphs. As a result, the ubiquity of GIFs has increaseddramatically extending across all social networking platforms and evenhaving a place in fashion advertising.

With the widespread popularity of animated GIFs, there is currently ahuge demand for easy-to-use tools that generate animated GIFs fromvideos. However, existing systems are cumbersome to use because theyrequire users to manually specify two timestamps, the beginning and theend of a video clip, from which a single animated GIF is generated. Suchconventional input can be performed either through a command lineinterface (CLI) or a graphical user interface (GUI). This requires auser to manually specify the exact time range which makes existingsystems difficult to use and requires extensive human effort andexperience.

For example, conventional systems require editors to manually extractindividual animated GIFs from videos and share them on social media.Tumblr®, for example, has an estimated 1.5 billion content videos acrossits sites. The conventional approach to GIF creation becomes quicklyimpractical when dealing with such figures, and there currently existsno alternative solution as manual generation is the only option theeditors have today.

As such, the instant disclosure provides a novel solution addressing theimmediate demand for an automated system, application and/or platformthat generates animated GIFs from videos. The present disclosureprovides novel systems and methods for automatic extraction and creationof animated GIF files from video files. According to some embodiments,the disclosed systems and methods first identifies a segment(s) of avideo file, interchangeably referred to as a “GIF candidate” or shot. Asegment of a video file is a portion of the video file, not the entirevideo. Identification of the GIF segment(s) is based on a determinationof shot boundaries within the video. Shot boundaries, which delineatethe beginning and end of a segment of the video file, are associatedwith transition frames within the video file that provide an indicationbetween differing scenes of the video's content. Such transition framescan include, but are not limited to, a cut between video frames, fadein/out between frames, dissolve or wipe effect(s), and/or any other typeof known or to be known visual effect that indicates a transitionbetween types content of a video file.

The disclosed systems and methods then evaluate each identified GIFcandidate in terms of “GIF quality” metrics, which results in a scorefor each GIF candidate. “GIF quality” metrics are associated with a GIFcandidate's attributes and/or features, which include, but are notlimited to, visual aesthetics, popularity, virality, memorability,sentiment, temporal nature of the content of the GIF, motion occurringwithin the segment as it is played, induced emotion as the segment isplayed, interestingness of the content of the segment, and the like. Forexample, the higher the quality score (e.g., popularity score of asegment), the higher the probability the animated GIF candidate istrending, or will become trending, for example, on social media. Whileevaluating a GIF candidate, the disclosed systems and methods alsodetermine an optimal playback speed for each GIF candidate—i.e., framesampling rate and the time interval between each frame.

As discussed below in more detail, based on the determinations of theGIF candidates' “GIF quality” and optimal playback speed, the disclosedsystems and methods can automatically create a GIF(s) from a video filethat can be rendered at its optimal playback speed. In some embodiments,a selection may also occur whereby the GIF candidate having the highest“GIF quality” can be selected for presentation to a user. In someembodiments, only those GIF candidates having a “GIF quality” satisfyinga threshold can be presented to a user, whereby these candidates canthen be selected by a user for rendering and/or sharing.

The benefits of the disclosed systems and methods can be evidencedtwo-fold: 1) the disclosed systems and methods provide a technologicallybased mechanism for automatic extraction of animated GIFs from videofiles; and (2) the proposed systems and methods are based on andtechniques that are specifically designed to evaluate the “GIF quality”of animated GIFs, for example, in terms of their induced popularity(i.e., how popular a GIF is or would become among the audience of socialnetworks), which has never been explored before. Thus, the disclosedsystems and methods, for example, extract GIFs from videos in a fullyautomated manner that is predicated on a determination of the “quality”of the GIF in a social networking setting.

The disclosed systems and methods can be implemented for any type ofcontent item, including, but not limited to, video, audio, images, text,and/or any other type of multimedia content. While the discussion hereinwill focus on video content items, it should not be construed aslimiting, as any type of content or multimedia content, whether known orto be known, can be utilized without departing from the scope of theinstant disclosure.

As discussed in more detail below at least in relation to FIG. 6,according to some embodiments, information associated with or derivedfrom created GIFs (or GIF candidates or shots), as discussed herein, canbe used for monetization purposes and targeted advertising whenproviding, delivering, sharing or enabling access to the created GIFs.Providing targeted advertising to users associated with such discoveredcontent can lead to an increased click-through rate (CTR) of such adsand/or an increase in the advertiser's return on investment (ROI) forserving such content provided by third parties (e.g., digitaladvertisement content provided by an advertiser, where the advertisercan be a third party advertiser, or an entity directly associated withor hosting the systems and methods discussed herein).

Certain embodiments will now be described in greater detail withreference to the figures. In general, with reference to FIG. 1, a system100 in accordance with an embodiment of the present disclosure is shown.FIG. 1 shows components of a general environment in which the systemsand methods discussed herein may be practiced. Not all the componentsmay be required to practice the disclosure, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the disclosure. As shown, system 100 of FIG.1 includes local area networks (“LANs”)/wide area networks(“WANs”)—network 105, wireless network 110, mobile devices (clientdevices) 102-104 and client device 101. FIG. 1 additionally includes avariety of servers, such as content server 106, application (or “App”)server 108, search server 120 and advertising (“ad”) server 130.

One embodiment of mobile devices 102-104 is described in more detailbelow. Generally, however, mobile devices 102-104 may include virtuallyany portable computing device capable of receiving and sending a messageover a network, such as network 105, wireless network 110, or the like.Mobile devices 102-104 may also be described generally as client devicesthat are configured to be portable. Thus, mobile devices 102-104 mayinclude virtually any portable computing device capable of connecting toanother computing device and receiving information. Such devices includemulti-touch and portable devices such as, cellular telephones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers, laptopcomputers, wearable computers, smart watch, tablet computers, phablets,integrated devices combining one or more of the preceding devices, andthe like. As such, mobile devices 102-104 typically range widely interms of capabilities and features. For example, a cell phone may have anumeric keypad and a few lines of monochrome LCD display on which onlytext may be displayed. In another example, a web-enabled mobile devicemay have a touch sensitive screen, a stylus, and an HD display in whichboth text and graphics may be displayed.

A web-enabled mobile device may include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application may be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually any webbased language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SMGL), HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message.

Mobile devices 102-104 also may include at least one client applicationthat is configured to receive content from another computing device. Theclient application may include a capability to provide and receivetextual content, graphical content, audio content, and the like. Theclient application may further provide information that identifiesitself, including a type, capability, name, and the like. In oneembodiment, mobile devices 102-104 may uniquely identify themselvesthrough any of a variety of mechanisms, including a phone number, MobileIdentification Number (MIN), an electronic serial number (ESN), or othermobile device identifier.

In some embodiments, mobile devices 102-104 may also communicate withnon-mobile client devices, such as client device 101, or the like. Inone embodiment, such communications may include sending and/or receivingmessages, searching for, viewing and/or sharing photographs, audioclips, video clips, or any of a variety of other forms ofcommunications. Client device 101 may include virtually any computingdevice capable of communicating over a network to send and receiveinformation. The set of such devices may include devices that typicallyconnect using a wired or wireless communications medium such as personalcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, or the like. Thus, client device 101may also have differing capabilities for displaying navigable views ofinformation.

Client devices 101-104 computing device may be capable of sending orreceiving signals, such as via a wired or wireless network, or may becapable of processing or storing signals, such as in memory as physicalmemory states, and may, therefore, operate as a server. Thus, devicescapable of operating as a server may include, as examples, dedicatedrack-mounted servers, desktop computers, laptop computers, set topboxes, integrated devices combining various features, such as two ormore features of the foregoing devices, or the like.

Wireless network 110 is configured to couple mobile devices 102-104 andits components with network 105. Wireless network 110 may include any ofa variety of wireless sub-networks that may further overlay stand-alonead-hoc networks, and the like, to provide an infrastructure-orientedconnection for mobile devices 102-104. Such sub-networks may includemesh networks, Wireless LAN (WLAN) networks, cellular networks, and thelike.

Network 105 is configured to couple content server 106, applicationserver 108, or the like, with other computing devices, including, clientdevice 101, and through wireless network 110 to mobile devices 102-104.Network 105 is enabled to employ any form of computer readable media forcommunicating information from one electronic device to another. Also,network 105 can include the Internet in addition to local area networks(LANs), wide area networks (WANs), direct connections, such as through auniversal serial bus (USB) port, other forms of computer-readable media,or any combination thereof. On an interconnected set of LANs, includingthose based on differing architectures and protocols, a router acts as alink between LANs, enabling messages to be sent from one to another,and/or other computing devices.

Within the communications networks utilized or understood to beapplicable to the present disclosure, such networks will employ variousprotocols that are used for communication over the network. Signalpackets communicated via a network, such as a network of participatingdigital communication networks, may be compatible with or compliant withone or more protocols. Signaling formats or protocols employed mayinclude, for example, TCP/IP, UDP, QUIC (Quick UDP Internet Connection),DECnet, NetBEUI, IPX, APPLETALK™, or the like. Versions of the InternetProtocol (IP) may include IPv4 or IPv6. The Internet refers to adecentralized global network of networks. The Internet includes localarea networks (LANs), wide area networks (WANs), wireless networks, orlong haul public networks that, for example, allow signal packets to becommunicated between LANs. Signal packets may be communicated betweennodes of a network, such as, for example, to one or more sites employinga local network address. A signal packet may, for example, becommunicated over the Internet from a user site via an access nodecoupled to the Internet. Likewise, a signal packet may be forwarded vianetwork nodes to a target site coupled to the network via a networkaccess node, for example. A signal packet communicated via the Internetmay, for example, be routed via a path of gateways, servers, etc. thatmay route the signal packet in accordance with a target address andavailability of a network path to the target address.

According to some embodiments, the present disclosure may also beutilized within or accessible to an electronic social networking site. Asocial network refers generally to an electronic network of individuals,such as acquaintances, friends, family, colleagues, or co-workers, thatare coupled via a communications network or via a variety ofsub-networks. Potentially, additional relationships may subsequently beformed as a result of social interaction via the communications networkor sub-networks. In some embodiments, multi-modal communications mayoccur between members of the social network. Individuals within one ormore social networks may interact or communication with other members ofa social network via a variety of devices. Multi-modal communicationtechnologies refers to a set of technologies that permit interoperablecommunication across multiple devices or platforms, such as cell phones,smart phones, tablet computing devices, phablets, personal computers,televisions, set-top boxes, SMS/MMS, email, instant messenger clients,forums, social networking sites, or the like.

In some embodiments, the disclosed networks 110 and/or 105 may comprisea content distribution network(s). A “content delivery network” or“content distribution network” (CDN) generally refers to a distributedcontent delivery system that comprises a collection of computers orcomputing devices linked by a network or networks. A CDN may employsoftware, systems, protocols or techniques to facilitate variousservices, such as storage, caching, communication of content, orstreaming media or applications. A CDN may also enable an entity tooperate or manage another's site infrastructure, in whole or in part.

The content server 106 may include a device that includes aconfiguration to provide content via a network to another device. Acontent server 106 may, for example, host a site or service, such asstreaming media site/service (e.g., YouTube®), an email platform orsocial networking site, or a personal user site (such as a blog, vlog,online dating site, and the like). A content server 106 may also host avariety of other sites, including, but not limited to business sites,educational sites, dictionary sites, encyclopedia sites, wikis,financial sites, government sites, and the like. Devices that mayoperate as content server 106 include personal computers desktopcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, servers, and the like.

Content server 106 can further provide a variety of services thatinclude, but are not limited to, streaming and/or downloading mediaservices, search services, email services, photo services, web services,social networking services, news services, third-party services, audioservices, video services, instant messaging (IM) services, SMS services,MMS services, FTP services, voice over IP (VOIP) services, or the like.Such services, for example a video application and/or video platform,can be provided via the application server 108, whereby a user is ableto utilize such service upon the user being authenticated, verified oridentified by the service. Examples of content may include images, text,audio, video, or the like, which may be processed in the form ofphysical signals, such as electrical signals, for example, or may bestored in memory, as physical states, for example.

An ad server 130 comprises a server that stores online advertisementsfor presentation to users. “Ad serving” refers to methods used to placeonline advertisements on websites, in applications, or other placeswhere users are more likely to see them, such as during an onlinesession or during computing platform use, for example. Variousmonetization techniques or models may be used in connection withsponsored advertising, including advertising associated with user. Suchsponsored advertising includes monetization techniques includingsponsored search advertising, non-sponsored search advertising,guaranteed and non-guaranteed delivery advertising, adnetworks/exchanges, ad targeting, ad serving and ad analytics. Suchsystems can incorporate near instantaneous auctions of ad placementopportunities during web page creation, (in some cases in less than 500milliseconds) with higher quality ad placement opportunities resultingin higher revenues per ad. That is advertisers will pay higheradvertising rates when they believe their ads are being placed in oralong with highly relevant content that is being presented to users.Reductions in the time needed to quantify a high quality ad placementoffers ad platforms competitive advantages. Thus higher speeds and morerelevant context detection improve these technological fields.

For example, a process of buying or selling online advertisements mayinvolve a number of different entities, including advertisers,publishers, agencies, networks, or developers. To simplify this process,organization systems called “ad exchanges” may associate advertisers orpublishers, such as via a platform to facilitate buying or selling ofonline advertisement inventory from multiple ad networks. “Ad networks”refers to aggregation of ad space supply from publishers, such as forprovision en masse to advertisers. For web portals like Yahoo!®,advertisements may be displayed on web pages or in apps resulting from auser-defined search based at least in part upon one or more searchterms. Advertising may be beneficial to users, advertisers or webportals if displayed advertisements are relevant to interests of one ormore users. Thus, a variety of techniques have been developed to inferuser interest, user intent or to subsequently target relevantadvertising to users. One approach to presenting targeted advertisementsincludes employing demographic characteristics (e.g., age, income, sex,occupation, etc.) for predicting user behavior, such as by group.Advertisements may be presented to users in a targeted audience based atleast in part upon predicted user behavior(s).

Another approach includes profile-type ad targeting. In this approach,user profiles specific to a user may be generated to model userbehavior, for example, by tracking a user's path through a web site ornetwork of sites, and compiling a profile based at least in part onpages or advertisements ultimately delivered. A correlation may beidentified, such as for user purchases, for example. An identifiedcorrelation may be used to target potential purchasers by targetingcontent or advertisements to particular users. During presentation ofadvertisements, a presentation system may collect descriptive contentabout types of advertisements presented to users. A broad range ofdescriptive content may be gathered, including content specific to anadvertising presentation system. Advertising analytics gathered may betransmitted to locations remote to an advertising presentation systemfor storage or for further evaluation. Where advertising analyticstransmittal is not immediately available, gathered advertising analyticsmay be stored by an advertising presentation system until transmittal ofthose advertising analytics becomes available.

Servers 106, 108, 120 and 130 may be capable of sending or receivingsignals, such as via a wired or wireless network, or may be capable ofprocessing or storing signals, such as in memory as physical memorystates. Devices capable of operating as a server may include, asexamples, dedicated rack-mounted servers, desktop computers, laptopcomputers, set top boxes, integrated devices combining various features,such as two or more features of the foregoing devices, or the like.Servers may vary widely in configuration or capabilities, but generally,a server may include one or more central processing units and memory. Aserver may also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

In some embodiments, users are able to access services provided byservers 106, 108, 120 and/or 130. This may include in a non-limitingexample, authentication servers, search servers, email servers, socialnetworking services servers, SMS servers, IM servers, MMS servers,exchange servers, photo-sharing services servers, and travel servicesservers, via the network 105 using their various devices 101-104. Insome embodiments, applications, such as a streaming video application(e.g., YouTube®, Netflix®, Hulu®, iTunes®, Amazon Prime®, HBO Go®, andthe like), blog, photo storage/sharing application or social networkingapplication (e.g., Flickr®, Tumblr®, and the like), can be hosted by theapplication server 108 (or content server 106, search server 120 and thelike). Thus, the application server 108 can store various types ofapplications and application related information including applicationdata and user profile information (e.g., identifying and behavioralinformation associated with a user). It should also be understood thatcontent server 106 can also store various types of data related to thecontent and services provided by content server 106 in an associatedcontent database 107, as discussed in more detail below. Embodimentsexist where the network 105 is also coupled with/connected to a TrustedSearch Server (TSS) which can be utilized to render content inaccordance with the embodiments discussed herein. Embodiments existwhere the TSS functionality can be embodied within servers 106, 108, 120and/or 130.

Moreover, although FIG. 1 illustrates servers 106, 108, 120 and 130 assingle computing devices, respectively, the disclosure is not solimited. For example, one or more functions of servers 106, 108, 120and/or 130 may be distributed across one or more distinct computingdevices. Moreover, in one embodiment, servers 106, 108, 120 and/or 130may be integrated into a single computing device, without departing fromthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating a client device showing anexample embodiment of a client device that may be used within thepresent disclosure. Client device 200 may include many more or lesscomponents than those shown in FIG. 2. However, the components shown aresufficient to disclose an illustrative embodiment for implementing thepresent disclosure. Client device 200 may represent, for example, clientdevices discussed above in relation to FIG. 1.

As shown in the figure, Client device 200 includes a processing unit(CPU) 222 in communication with a mass memory 230 via a bus 224. Clientdevice 200 also includes a power supply 226, one or more networkinterfaces 250, an audio interface 252, a display 254, a keypad 256, anilluminator 258, an input/output interface 260, a haptic interface 262,an optional global positioning systems (GPS) receiver 264 and acamera(s) or other optical, thermal or electromagnetic sensors 266.Device 200 can include one camera/sensor 266, or a plurality ofcameras/sensors 266, as understood by those of skill in the art. Thepositioning of the camera(s)/sensor(s) 266 on device 200 can change perdevice 200 model, per device 200 capabilities, and the like, or somecombination thereof.

Power supply 226 provides power to Client device 200. A rechargeable ornon-rechargeable battery may be used to provide power. The power mayalso be provided by an external power source, such as an AC adapter or apowered docking cradle that supplements and/or recharges a battery.

Client device 200 may optionally communicate with a base station (notshown), or directly with another computing device. Network interface 250includes circuitry for coupling Client device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies as discussed above. Network interface 250 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 252 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 252 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action. Display 254 may be a liquid crystal display (LCD), gasplasma, light emitting diode (LED), or any other type of display usedwith a computing device. Display 254 may also include a touch sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 256 may comprise any input device arranged to receive input froma user. For example, keypad 256 may include a push button numeric dial,or a keyboard. Keypad 256 may also include command buttons that areassociated with selecting and sending images. Illuminator 258 mayprovide a status indication and/or provide light. Illuminator 258 mayremain active for specific periods of time or in response to events. Forexample, when illuminator 258 is active, it may backlight the buttons onkeypad 256 and stay on while the client device is powered. Also,illuminator 258 may backlight these buttons in various patterns whenparticular actions are performed, such as dialing another client device.Illuminator 258 may also cause light sources positioned within atransparent or translucent case of the client device to illuminate inresponse to actions.

Client device 200 also comprises input/output interface 260 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 2. Input/output interface 260 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like. Haptic interface 262 is arranged to providetactile feedback to a user of the client device. For example, the hapticinterface may be employed to vibrate client device 200 in a particularway when the Client device 200 receives a communication from anotheruser.

Optional GPS transceiver 264 can determine the physical coordinates ofClient device 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 264 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), E-OTD, CI, SAI, ETA, BSS or thelike, to further determine the physical location of Client device 200 onthe surface of the Earth. It is understood that under differentconditions, GPS transceiver 264 can determine a physical location withinmillimeters for Client device 200; and in other cases, the determinedphysical location may be less precise, such as within a meter orsignificantly greater distances. In one embodiment, however, Clientdevice may through other components, provide other information that maybe employed to determine a physical location of the device, includingfor example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory 230 includes a RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates another example of computer storage mediafor storage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory 230 stores abasic input/output system (“BIOS”) 240 for controlling low-leveloperation of Client device 200. The mass memory also stores an operatingsystem 241 for controlling the operation of Client device 200. It willbe appreciated that this component may include a general purposeoperating system such as a version of UNIX, or LINUX™, or a specializedclient communication operating system such as Windows Client™, or theSymbian® operating system. The operating system may include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Memory 230 further includes one or more data stores, which can beutilized by Client device 200 to store, among other things, applications242 and/or other data. For example, data stores may be employed to storeinformation that describes various capabilities of Client device 200.The information may then be provided to another device based on any of avariety of events, including being sent as part of a header during acommunication, sent upon request, or the like. At least a portion of thecapability information may also be stored on a disk drive or otherstorage medium (not shown) within Client device 200.

Applications 242 may include computer executable instructions which,when executed by Client device 200, transmit, receive, and/or otherwiseprocess audio, video, images, and enable telecommunication with a serverand/or another user of another client device. Other examples ofapplication programs or “apps” in some embodiments include browsers,calendars, contact managers, task managers, transcoders, photomanagement, database programs, word processing programs, securityapplications, spreadsheet programs, games, search programs, and soforth. Applications 242 may further include search client 245 that isconfigured to send, to receive, and/or to otherwise process a searchquery and/or search result using any known or to be known communicationprotocols. Although a single search client 245 is illustrated it shouldbe clear that multiple search clients may be employed. For example, onesearch client may be configured to enter a search query message, whereanother search client manages search results, and yet another searchclient is configured to manage serving advertisements, IMs, emails, andother types of known messages, or the like.

Having described the components of the general architecture employedwithin the disclosed systems and methods, the components' generaloperation with respect to the disclosed systems and methods will now bedescribed below.

FIG. 3 is a block diagram illustrating the components for performing thesystems and methods discussed herein. FIG. 3 includes a GIF engine 300,network 315 and database 320. The GIF engine 300 can be a specialpurpose machine or processor and could be hosted by an applicationserver, content server, social networking server, web server, searchserver, content provider, email service provider, ad server, user'scomputing device, and the like, or any combination thereof.

According to some embodiments, GIF engine 300 can be embodied as astand-alone application that executes on a user device. In someembodiments, the GIF engine 300 can function as an application installedon the user's device, and in some embodiments, such application can be aweb-based application accessed by the user device over a network. Insome embodiments, the GIF engine 300 can be installed as an augmentingscript, program or application to another media application (e.g.,Yahoo!® Video, YouTube®, Hulu®, and the like).

The database 320 can be any type of database or memory, and can beassociated with a content server on a network (e.g., content server 106,search server 120 or application server 108 from FIG. 1) or a user'sdevice (e.g., device 101-104 or device 200 from FIGS. 1-2). Database 320comprises a dataset of data and metadata associated with local and/ornetwork information related to users, services, applications, content(e.g., video) and the like. Such information can be stored and indexedin the database 320 independently and/or as a linked or associateddataset. As discussed above, it should be understood that the data (andmetadata) in the database 320 can be any type of information and type,whether known or to be known, without departing from the scope of thepresent disclosure.

According to some embodiments, database 320 can store data for users,e.g., user data. According to some embodiments, the stored user data caninclude, but is not limited to, information associated with a user'sprofile, user interests, user behavioral information, user attributes,user preferences or settings, user demographic information, userlocation information, user biographic information, and the like, or somecombination thereof. In some embodiments, the user data can alsoinclude, for purposes creating, recommending, rendering and/ordelivering GIFs or videos, user device information, including, but notlimited to, device identifying information, device capabilityinformation, voice/data carrier information, Internet Protocol (IP)address, applications installed or capable of being installed orexecuted on such device, and/or any, or some combination thereof. Itshould be understood that the data (and metadata) in the database 320can be any type of information related to a user, content, a device, anapplication, a service provider, a content provider, whether known or tobe known, without departing from the scope of the present disclosure.

According to some embodiments, database 320 can store data and metadataassociated with video content from an assortment of media providers. Forexample, the information can be related to, but not limited to, contenttype of the video, a category associated with the video, informationassociated with the pixels and frames of the videos, and any other typeof known or to be known attribute or feature associated with a videofile. Additionally, the video information in database 320 for each videocan comprise, but is not limited to, attributes including, but notlimited to, popularity of the video, quality of the video, recency ofthe video (when it was published, shared, edited and the like), and thelike. Such factors can be derived from information provided by the user,a service provider (i.e., Yahoo!® or Tumblr®), by the content/serviceproviders providing video content (e.g., Netflix®, Hulu®, YouTube®), orby other third party services (e.g., rottentomatoes.com, IMDB™,Facebook®, Twitter® and the like), or some combination thereof.

According to some embodiments, such video information can be representedas an n-dimensional vector (or feature vector) for each video, where theinformation associated with the video can be translated as a node on then-dimensional vector. Database 320 can store and index video informationin database 320 as linked set of video data and metadata, where the dataand metadata relationship can be stored as the n-dimensional vector.Such storage can be realized through any known or to be known vector orarray storage, including but not limited to, a hash tree, queue, stack,VList, or any other type of known or to be known dynamic memoryallocation technique or technology.

While the discussion below will involve vector analysis of videoinformation, as discussed above, the video information can be analyzed,stored and indexed according to any known or to be known computationalanalysis technique or algorithm, such as, but not limited to, clusteranalysis, data mining, Bayesian network analysis, Hidden Markov models,artificial neural network analysis, logical model and/or tree analysis,and the like.

For purposes of the present disclosure, as discussed above, videos(which are stored and located in database 320) as a whole are discussedwithin some embodiments; however, it should not be construed to limitthe applications of the systems and methods discussed herein. That is,while reference is made throughout the instant disclosure to videos(e.g., video clips, movies, music videos, TV shows, YouTube® videos,Instagram® videos, Vine™ videos, and/or any other type of streaming ordownloadable video content), other forms of user generated content andassociated information, including for example text, audio, multimedia,RSS feed information can be used without departing from the scope of theinstant application, which can thereby be communicated and/or accessedand processed by the GIF engine 300 according to the systems and methodsdiscussed herein.

As discussed above, with reference to FIG. 1, the network 315 can be anytype of network such as, but not limited to, a wireless network, a localarea network (LAN), wide area network (WAN), the Internet, or acombination thereof. The network 315 facilitates connectivity of the GIFengine 300, and the database of stored resources 320. Indeed, asillustrated in FIG. 3, the GIF engine 300 and database 320 can bedirectly connected by any known or to be known method of connectingand/or enabling communication between such devices and resources.

The principal processor, server, or combination of devices thatcomprises hardware programmed in accordance with the special purposefunctions herein is referred to for convenience as GIF engine 300, andincludes shot boundary detection module 302, shot evaluation module 304,shot selection module 306, and generation module 308. It should beunderstood that the engine(s) and modules discussed herein arenon-exhaustive, as additional or fewer engines and/or modules (orsub-modules) may be applicable to the embodiments of the systems andmethods discussed. The operations, configurations and functionalities ofeach module, and their role within embodiments of the present disclosurewill be discussed with reference to FIG. 4.

As discussed in more detail below, the information processed by the GIFengine 300 can be supplied to the database 320 in order to ensure thatthe information housed in the database 320 is up-to-date as thedisclosed systems and methods leverage real-time information and/orbehavior associated with the video file, user and/or the user's deviceduring or responsive to GIF creation, selection and rendering, asdiscussed in more detail below.

Turning to FIG. 4, Processes 400 details steps performed in accordancewith exemplary embodiments of the present disclosure for automaticallyextracting and creating an animated Graphics Interchange Format (GIF)file from a video file. According to some embodiments, as discussedherein with relation to FIG. 4, Process 400 involves automaticallyextracting content from a video file in order to create a GIF file fromthe extracted content. Such extraction and creation involves, detectingboundaries between shots of a video; determining the optimal playbacksetting of animated GIFs; and analyzing motion, emotion, andinterestingness of a shot, among other features and attributes, in orderto determine the induced popularity of animated GIFs, as discussed inmore detail below.

Process 400 beings with Step 402 where a video file is identified. Step402's identification of a video file can be based on a user's request toview or preview the video file, the identification of the video fileduring a recommendation process, or a request from the user to generateda GIF from the video file, among other known or to be known processesthat involve identification of a video file for presentation of at leasta portion of the video file to the user.

Step 404 of Process 400, which is performed by the shot boundarydetection module 302 of GIF engine 300, involves identifying at leastone GIF candidate from within the video file by identifying shotboundaries in the video file. In other words, Step 404 identifiesboundaries between shots in a video file, where a shot is a set offrames that are temporally adjacent in the video file and visuallycoherent. As discussed above, a GIF candidate (interchangeably referredto as a shot) is a segment of the video file that is delineated by abeginning shot boundary and an ending shot boundary. According to someembodiments, shot boundaries are transition frames within the videofile, and include, but are not limited to, a cut between video frames,fade in/out between frames, dissolve or wipe effect(s), and/or any othertype of known or to be known effect that transitions between scenes of avideo file.

According to some embodiments, Step 404 involves analyzing the videofile to determine each transition frame. Such analysis can includeparsing the video file and analyzing each frame (or pixel of each frame)to identify discontinuities between adjacent frames. In someembodiments, the shot boundary detection occurring in Step 404 involvesimplementing any known or to be known media frame algorithm or schemetechnique for determining differences between adjacent frames, such as,frame differencing and a multiple change point detection (MCPD)algorithm. As understood by those of skill in the art, such algorithmsand techniques compute the sum of pixel-wise differences from each pairof frames over time, and then determine the boundaries by thresholdingthe difference value with an empirically found value.

In some embodiments, applications of the frame differencing techniquefocus on identifying transition frames that comprise “easy-to-detect”transitions, such as, cut, wipe and the like. In order to identify themore subtle boundaries, such as for example, fade in/out, or dissolve,the analysis of each frame is refined by applying the MCPD algorithm.Therefore, according to some embodiments, Step 404 involves theapplication of the frame differencing algorithm, then an application ofthe MCPD algorithm in order to refine the results from the framedifferencing.

Thus, Step 404 involves analyzing the video file obtained from Step 402via the shot boundary detection module 302 applying a frame differencingtechnique in order to identify the presence of transitions in the framesof the video file. The result of the frame differencing techniqueincludes identification of the transition frames associated with cut,wipe, and similarly basic visual effects. This, therefore, provides aninitial result of the segments (or shots) within the video file. Next,for each segment (or shot) that is longer than a predetermined length,the MCPD algorithm is applied. This application occurs because, as notedabove, frame differencing cannot identify the more subtle transitioneffects of a video due to its efficient techniques of analyzing media;therefore, the shot boundary detection module 302 implements a morecomplex algorithm (i.e., MCPD) to identify transitions (e.g., dissolveand wipe) within the segments/shots that are longer than a predeterminedlength (e.g., 10 seconds), as such subtle transitions may have wentundetected during the initial frame differencing analysis. In someembodiments, if there are no shots longer than the predetermined length,the analysis stops as all transition frames are understood to belocated.

Therefore, according to some embodiments, the shot boundary detectionmodule 302 applies a “divide-and-conquer” approach to its analysis ofthe video file by “dividing” (or parsing) the video file in to segmentsusing an efficient algorithm (e.g., frame differencing), then“conquering” each small piece using a comprehensive algorithm (e.g.,MCPD algorithm). As a result of Step 404, shot boundaries within a videofile are detected, which are represented as time indices within thevideo file, and as a result, reference to each shot (or segment or GIFcandidate) can be made from sequential shot boundary pairs.

In some embodiments, Step 404 may involve applying the framedifferencing and MCPD algorithms at the same time; or, may involve onlyapplying the MCPD algorithm. In some embodiments, Step 404 may involveapplying any known or to be known algorithm that can identify all typesof known or to be known transitions within a media file thereby avoidingthe two-step process of Step 404 discussed above.

By way of a non-limiting example, FIG. 5 illustrates the discussionabove respective to Steps 402-404. FIG. 5 illustrates a video file 500that is obtained (Step 402), whereby, for example, the video 500 hasimage frames 1-12. The video, for example, includes content showing areporter speaking in front of a court house (frames 1-3), then thecontent transitions (frame 4) to a clip of a couple getting married(frames 5-8), then finally transitions (frame 9) back to the reporter(frames 10-12). The shot boundary detection of Step 404 involvesanalyzing the frames of the video to not only identify when thetransitions occur (frames 4 and 9), but also to identify each segment ofthe video bookended by the transitions. The two transitions that areidentified correspond to the video cutting from the reporter (frame 4,item 508 which represents the image frame where the transition effectoccurs), then cutting back to the reporter (frame 9, item 510 whichrepresents the image frame where the transition effect occurs). Thus,there are three shots (or segments) in the video delineated by the twoidentified transitions: 1) the first shot of the reporter (frames 1-3,item 502); 2) the clip of the wedding (frames 5-8, item 504); and 3) thesecond shot of the reporter (frames 10-12, item 506). In someembodiments, the true beginning and end of the video file can be viewedas transitions that indicate the beginning and end of the video file;therefore, there can actually be 4 transitions, where the beginning ofthe video file an the first transition bookend the first shot of thereporter and the second transition and the end of the video file bookendthe second shot of the reporter. In some embodiments, the transitionsframes may also be included in an identified shot, either the beginningtransition frame, ending transition frame, or some combination thereof.Thus, as discussed in more detail below, in the example of FIG. 5, thereare 3 shots (items 502-506), and each shot is then analyzed according tothe discussion below respective to Steps 406-412, whereby at least oneshot (items 502-506) can be converted to an animated GIF.

Continuing with Process 400, Step 406 involves analyzing each GIFcandidate identified from Step 404 and determining its popularity. Step406 is performed by the shot evaluation module 304. Step 406'spopularity determination is associated with a determination of a GIFcandidate's optimal playback settings. As discussed herein, the optimalplayback settings are based on the shot's “quality” which corresponds tothe shot's features, which include motion, emotion, and interestingness,as discussed herein.

Step 406's GIF candidate (or shot) evaluation involves four parts: 1)generating multiple playback settings, 2) feature extraction, 3) featurefusion, and 4) popularity score estimation. Turning to Part 1 of Step406: a shot can be transformed into an animated GIF using differentplayback settings; therefore, by adjusting the frame sampling rate andthe time interval between two frames the shot can be played faster orslower, at different frame rates. Since an animated GIF can look verydifferent based on its playback setting, its popularity may change evenif it was generated from the same shot (e.g., playing too fast or slowwould render the GIF unwatchable or uninteresting); therefore, each GIFcandidate identified from Step 404 can have multiple different versionscreated in Part 1 of Step 406, where each version has a differentplayback settings.

Part 1 of Step 406 involves adjusting the frame sampling rate of a shotby specifying the step size between frames within the shot. For example,a given shot has 30 frames x₁, x₂, x₃, . . . , x₃₀. Using a step size ofone, the animated GIF will retain all the original 30 frames; using astep size two, every other frame is skipped and the animated GIF willcontain half the number of the original frames—i.e., x₁, x₃, x₅, . . .x₂₉.

Part 1 of Step 406 also involves adjusting the time interval between twoadjacent frames. As understood by those of skill in the art, every videofile is encoded with this information, which is referred to by those ofskill in the art as frames per second (FPS). In some embodiments, thetime interval is varied by multiplying a positive real number to the FPSof the shot. Such positive real number can be set according to afactor(s) to ensure a varying playback speed. For example, the step sizebetween {1, 2, 3, . . . , 10} may be varied by changing the timeinterval by multiplying it by {⅕, ¼, ⅓, ½, 1, 2, 3, 4, 5}. As a result,90 different playback settings from a given shot can be generated.

As a result of Part 1 of Step 406, multiple playback settings for agiven shot identified from Step 404 are determined. The number ofplayback settings can be in accordance with a predetermined numbersetting set by a user, the system, and application, an administrator,and the like, or some combination thereof. That is, based on theadjustments to the frame sampling rate and the time intervals, multipleplayback rates for a shot are determined. The determination of multipleplayback rates (or settings) is utilized to select a given shot andgenerate an animated GIF, as discussed below.

Given an identified shot (or GIF candidate) from Step 404, and itsdetermined playback settings (from Part 1 of Step 406), Step 406 theninvolves performing feature extraction (Part 2 of Step 406) in order toextract three types of features from the shot: 1) motion, 2) emotion,and 3) interestingness.

Motion plays an important role in animated GIFs, as without motion, ananimated GIF would merely be a static image. Motion can be described ina variety of different ways, and for purposes of this disclosure, motionis characterized by attributes corresponding to a) shape and appearancechanges over time, b) the total aggregate motion energy, and c) the looplikelihood.

To measure the first attribute of motion: a) the shape and theappearance changes over time, the shot evaluation module 304 parses ashot and identifies each frame (or image frame). The shot evaluationmodule 304 then samples points (or positions, or pixels) within eachframe and estimates an optical flow over time in order to obtain itstrajectory. Such sampling is performed by utilizing any known or to beknown efficient solution based on density trajectories or any otherknown or to be known technique based on optical flow in order todetermine a pattern of apparent motion of objects, surfaces, and edgesin a frame. For example, such techniques can include, but are notlimited to, phase correlation, discrete optimization algorithms anddifferential optical flow estimates, such as, but not limited to, aLucas-Kanade method, a Horn-Schunck method, a Buxton-Buxton method, aBlack-Jepson method, and the like, or any other known or to be knownmethodology. The shot evaluation module 304 then computes, from eachtrajectory, three types of feature descriptors to describe the shape andappearance of motion over time: 1) histograms of oriented gradients, 2)histograms of optical flows, and 3) histograms of motion boundaries.

To measure the second attribute of motion: b) the total aggregate motionenergy, there are many known (and to be known) techniques. In someembodiments, the shot evaluation module 304 can determine a pixel-wisedifference value between frames over the entire duration of a shot andthen aggregate such difference value. In some embodiments, the shotevaluation module 304 can compute dense trajectories (as discussedabove) and aggregate the total displacement of such trajectories. Assuch, the higher the measure of total displacement, the more dynamic themotion of the shot.

To measure the third attribute of motion: c) the loop likelihood, thereare also many known (and to be known) techniques. In a similar manner asdetermining the measure of b) the total energy, the shot evaluationmodule 304 can determine an aggregate of a pixel-wise difference valuebetween frames of the shot, or determine an aggregate of the totaldisplacement of the dense trajectories. However, the difference for c)the loop likelihood is that instead of considering the entirety offrames of the shot, the shot evaluation module 304 focuses specificallyon the shot boundaries (i.e., the beginning and end of a shot, as it isthe beginning and end parts of the shot that characterize the loopingbehavior). Therefore, in some embodiments, the shot evaluation module304 can measure the sum of pixel-wise difference values between thefirst and the last frame of a shot, or a window (e.g., predeterminedrange) of frames at the beginning and end of the shot. In someembodiments, the shot evaluation module 304 can compute the optical flowbetween the first and the last frame (or their associated windows), andmeasure the total optical flow displacements.

Turning to the emotion feature of a shot, in some embodiments, the shotevaluation module 304 bases the emotion determination on six universallyrecognized basic emotions: anger, disgust, fear, happiness, sadness, andsurprise. In some embodiments, from a given shot, the shot evaluationmodule 304 determines the intensities of the six emotions and compiles asix-dimensional real vector, with the values normalized between 0 (lowintensity) and 1 (high intensity). In some embodiments, the emotiondetermination may include more or less emotions.

It should be understood that any type of known or to be known facialrecognition and/or action recognition algorithm or technique can beutilized to analyze a shot and determine emotional intensities from theshot—such as, but not limited to, geometric algorithms, photometricalgorithms, three-dimensional (3D) algorithms and/or skin-texturealgorithms. For example, such algorithms can include, but are notlimited to, principal component analysis using Eigen-faces, lineardiscriminate analysis, elastic bunch graph matching using the Fisherfacealgorithm, a Hidden Markov model, the Multilinear Subspace Learningusing tensor representation, a neuronal motivated dynamic link matching,and the like.

For example, according to some embodiments, the intensities of emotionscan be determined by detecting facial action units in a frame of ashot—facial action units are a standard set of human facial musclemovements used to systematically categorize the physical expression ofemotions. For example, it is well known that happiness can be describedas a combination of AU 6 (cheek raiser)+AU 12 (lip corner puller);therefore, by the shot evaluation module 304 applying any known or to beknown facial recognition technique or algorithm to the frame(s) of ashot, the happiness emotion can be recognized by detecting the presenceof AU6 and AU12.

The third type of features extracted in Part 2 of Step 406 is theinterestingness feature. The shot evaluation module 304 measures theinterestingness of a shot by the presence or absence of a set ofpredefined objects and actions. For example, the set may include certainobject categories such as the human face, cat, and dog; and also actioncategories such as dunk shot (basketball), touchdown (football), hole inone (golf), to name a few examples. Given a set of N object categoriesand M action categories, the shot evaluation module 304 analyzes theframes of a shot and detects each of them using any known or to be knownobject and action detection algorithm, such as, but not limited tofeature learning, vectorization, Gaussian recognition, Hidden MarkovModels (HMM), and the like. As a result of such analysis, the object andaction categories are translated into an (N+M)-dimensional real vectorwhich represents the interestingness of a shot, where each node on thevector indicates the confidence level of the presence of an object or anaction in the shot, normalized between a 0 (low confidence) and 1 (highconfidence) value.

Turning to Part 3 of Step 406, the shot evaluation module 304 combinesthe three types of features (i.e., motion, emotion and interestingness,discussed above) by concatenating them into a single feature vector. Itshould be understood by those of skill in the art that any type of knownor to be known principal component analysis and canonical correlationanalysis or vector analysis can be applied herein without departing fromthe scope of the instant disclosure. For example, the three features canbe translated into a single feature vector via a vector analysisalgorithm or technique, as well as any known or to be knowncomputational analysis technique or algorithm, such as, but not limitedto, cluster analysis, data mining, Bayesian network analysis, HiddenMarkov models, artificial neural network analysis, logical model and/ortree analysis, and the like. The output of Part 3 of Step 406 is asingle feature vector (referred to as the fusion feature vector) thathas a dimension that is fixed across shots with different lengths andplayback settings.

Part 4 of Step 406 involves determining (or determining a probability orestimating) an induced popularity score of a shot under a specificplayback setting by using any known or to be known regression functionthat maps the feature vector from Part 3 of Step 406 (i.e., the fusionfeature vector resultant of the fusion of the extracted features motion,emotion and interestingness) to a popularity score. Indeed, any known orto be known regression function can be utilized herein without departingfrom the scope of the instant disclosure, such as, for example, linearregression, support vector regression and the like.

Therefore, according to some embodiments, the shot evaluation module 304applies a regression function to the fusion feature vector X and aweighted (optimal) parameter W and returns a real valued output score y:F(X,W)=y. The weighted parameter W is based on a trained popularitymodel, as discussed below.

As understood by not only those of skill in the art, but also byeveryday users of the internet, users express their interests toanimated GIFs posted on social networking sites (e.g., Tumblr®) througha number of actions, such as, “like” or “reblog” (or sharing orreposts). The more likes or shares an animated GIF (or any content itemhas for that matter), the more popular it is. The trained popularitymodel utilizes this information as a proxy measure for popularity fromwhich parameter W is derived.

The GIF engine 300 leverages existing data resources from socialnetworking sites (e.g., Tumblr®) by analyzing a large number of animatedGIFs and their corresponding number of likes and shares (e.g., reblogs).For each GIF, motion, emotion and interestingness features are extractedand translated into their own fusion feature vector X—which is performedin a similar manner as discussed above.

For example, a training dataset of GIFs from Tumblr® is denoted as D:D={(X_(i), y_(i))} where y_(i) is the aggregate number of likes andreblogs for the i^(th) animated GIF in the dataset. The weightedparameter W is determined by solving:W=argmin_{W}\sum_{i}loss(y_i,F(X_i,W))+R(W),

where loss (y_(i), F(X_(i), W)) measures the discrepancy between theestimated and the actual popularity score. The second term, R(W),referred to as the regularizer, prevents overfitting the solution to agiven dataset by measuring the complexity of the solution W (where themore complex the solution is, the more probable the function willoverfit). For example, typical choices of the regularizer include L2 andL1 norms. In some embodiments as discussed above, any proper lossfunction for regression analysis (or support vector regression) can beused here, for example, e.g., squared loss:½(y_(i)−F(X_(i),W)){circumflex over ( )}2. By minimizing the expectedloss over the training dataset, the optimal solution W is determined,which best estimates the popularity of animated GIFs among an audience(e.g., users from the social networking site where the data set wascollected—e.g., Tumblr®).

Therefore, Part 4 of Step 406 as discussed above involves the shotevaluation module 304 applying a regression function to the fusionfeature vector X and a weighted (optimal) parameter W, and returning areal valued output score y: F(X,W)=y. Referring back to Part 1 of Step406, where the multiple playback settings are analyzed for a given shot,Part 4 of Step 406 involves identifying the popularity score for eachplayback setting in order to determine the optimal playback setting of ashot.

In summary of Parts 1-4 of Step 406, the shot evaluation module 304analyzes the shots identified from Step 404 in order to determinemultiple playback settings for each shot (Part 1). Then, each shot isanalyzed in order to extract features related to motion, emotion andinterestingness (Part 2). The extracted features are compiled into afusion feature vector (Part 3). Then, a regression algorithm is appliedto the fusion feature vector in order to determine a popularity scorefor each of the multiple playback settings (Part 4). Thus Step 406results in an evaluation of the shots (or GIF candidates) of a videofile that provides an indication of the popularity scores for each shot,at differing playback speeds.

In step 408, the shot selection module 306 selects a predeterminednumber of top scoring shots for animated GIF generation. The selectedshot(s) is formatted according to an optimal playback setting (selectedfrom the multiple playback settings of the shot), which is determined bysuch shot(s) version having the highest popularity score(s). In someembodiments, the number of shots selected can be set by a user, thesystem, device or network capabilities for rendering a GIF, anapplication, an administrator, and the like, or some combinationthereof. In some embodiments, the number of top scoring shots mustsatisfy a popularity threshold, in that only shots with popularityscores at or above the popularity threshold are eligible for selection.In some embodiments, if the GIF engine 300 is requested to generate aspecific number of GIFs from a video file (e.g., by a request from auser), then only the top scoring shots corresponding to that specificnumber are selected (e.g., if a single GIF is requested, then the shotwith the highest score is selected). In some embodiments, the selectionof shot can be performed by a user, where multiple shots are presentedto a user, and the user can select the shot(s) he or she desires toconvert into an animated GIF.

In some embodiments, when multiple GIFs are requested, in order toensure that the resulting animated GIFs are non-redundant, the shotselection module 306 can perform any known or to be known clusteringanalysis, where the number of clusters set is the number of animatedGIFs to be generated (or requested), and the only one animated GIF percluster can be selected.

In Step 410, the generation module 308 transforms the selected shot(s)(or selected GIF candidate) into an animated GIF. That is, the animatedGIF is created from the selected shot(s). Thus, the shot with theoptimal playback speed (based on the associated popularity score) isconverted into an animated GIF. The generation module 308 can create theanimated GIF using any known or to be known shot/segment transformationtechnique, such as, but not limited to, imagemagick and gifsiclelibraries, to name a few examples.

In Step 412, the generated animated GIF(s) is communicated to a user fordisplay on the user's device. In some embodiments, such communicationcan involve automatically rendering the GIF upon display on the user'sdevice, and in some embodiments, such communication can involve a usersharing the GIF with another user. In some embodiments, sharing of theGIF with an identified set of users can be performed automatically upongeneration of the GIF, where not only does the requesting user receivethe GIF, but also other users who follow the user, or have beenidentified by the user, can be provided the generated GIF (e.g.,reblogging the GIF to a user's followers pages on Tumblr®). As will beunderstood by those of skill in the art, sharing GIFs extracted fromvideos in this manner could result in improved user engagement in videocontent from which the GIF was created and/or the generated GIFs, aswell as increased activity by users on a social networking site thathosts and/or creates such GIFs.

According to some embodiments of the present disclosure, informationassociated with a extracted/created GIF, as discussed above in relationto Process 400, can be fed back to the GIF engine 300 for modeling (ortraining) of the information stored in database 320 via iterative orrecursive bootstrapping or aggregation functionality. This can improvethe accuracy of popularity scores for GIF candidates (i.e. shots) and/orthe selection of the optimal playback speeds for particular types of GIFfiles, as discussed above. Embodiments of the present disclosure involvethe recommendation engine 300 applying such recursive/bootstrappingfunctions utilizing any known or to be known open source and/orcommercial software machine learning algorithm, technique or technology.

FIG. 6 is a work flow example 600 for serving relevant digital contentassociated with advertisements (e.g., advertisement content) based onthe information associated with a created GIF, as discussed above inrelation to FIGS. 3-4. Such information, referred to as “GIFinformation” for reference purposes only, can include, but is notlimited to, the identity of the video from which the GIF was created,the attributes of the video from which the GIF was created, attributesof the GIF, the content of the GIF, and the like, and/or somecombination thereof.

As discussed herein, reference to an “advertisement” should beunderstood to include, but not be limited to, digital content thatprovides information provided by another user, service, third party,entity, and the like. Such digital ad content can include any type ofmedia renderable by a computing device, including, but not limited to,video, text, audio, images, and/or any other type of known or to beknown multi-media. In some embodiments, the digital ad content can beformatted as hyperlinked multi-media content that provides deep-linkingfeatures and/or capabilities.

By way of a non-limiting example, work flow 600 includes a user beingprovided with a GIF that displays looping video content of a touchdownfrom the Super Bowl®. Based on information related to the determinationthat the GIF relates to the sport of football, specifically the NFL®,for example, the user may be provided with digital ad content related tothe purchase of NFL merchandise.

In Step 602, GIF information associated with a created GIF file isidentified. As discussed above, the GIF information can be based on theGIF creation process outlined above with respect to FIGS. 3-4. Forpurposes of this disclosure, Process 600 will refer to single GIF fileas the basis for serving an advertisement(s); however, it should not beconstrued as limiting, as any number of GIFs, and/or quantities ofinformation related to users and their interaction with created GIFs canform such basis, without departing from the scope of the instantdisclosure.

In Step 604, a context is determined based on the identified GIFinformation. This context forms a basis for serving advertisementsrelated to the GIF information. In some embodiments, the context can bedetermined by determining a category which the GIF information of Step602 represents. For example, the category can be related to the type ofvideo from which the GIF was created, and/or can be related to thecontent type of the GIF file. In some embodiments, the identification ofthe context from Step 604 can occur before, during and/or after theanalysis detailed above with respect to Process 400, or some combinationthereof.

In Step 606, the context (e.g., content/context data) is communicated(or shared) with an advertisement platform comprising an advertisementserver 130 and ad database. Upon receipt of the context, theadvertisement server 130 performs a search for a relevant advertisementwithin the associated ad database. The search for an advertisement isbased at least on the identified context.

In Step 608, the advertisement server 130 searches the ad database foran advertisement(s) that matches the identified context. In Step 610, anadvertisement is selected (or retrieved) based on the results of Step608. In some embodiments, the selected advertisement can be modified toconform to attributes of the page, message or method upon which theadvertisement will be displayed, and/or to the application and/or devicefor which it will be displayed. In some embodiments, the selectedadvertisement is shared or communicated via the application the user isutilizing to render the GIF. Step 612. In some embodiments, the selectedadvertisement is sent directly to each user's computing device. In someembodiments, the selected advertisement is displayed in conjunction witha displayed GIF on the user's device and/or within the application beingused to identify, select and/or render the GIF file.

As shown in FIG. 7, internal architecture 700 of a computing device(s),computing system, computing platform, user devices, set-top box, smartTV and the like includes one or more processing units, processors, orprocessing cores, (also referred to herein as CPUs) 712, which interfacewith at least one computer bus 702. Also interfacing with computer bus702 are computer-readable medium, or media, 706, network interface 714,memory 704, e.g., random access memory (RAM), run-time transient memory,read only memory (ROM), media disk drive interface 720 as an interfacefor a drive that can read and/or write to media including removablemedia such as floppy, CD-ROM, DVD, media, display interface 710 asinterface for a monitor or other display device, keyboard interface 716as interface for a keyboard, pointing device interface 718 as aninterface for a mouse or other pointing device, and miscellaneous otherinterfaces not shown individually, such as parallel and serial portinterfaces and a universal serial bus (USB) interface.

Memory 704 interfaces with computer bus 702 so as to provide informationstored in memory 704 to CPU 712 during execution of software programssuch as an operating system, application programs, device drivers, andsoftware modules that comprise program code, and/or computer executableprocess steps, incorporating functionality described herein, e.g., oneor more of process flows described herein. CPU 712 first loads computerexecutable process steps from storage, e.g., memory 704, computerreadable storage medium/media 706, removable media drive, and/or otherstorage device. CPU 712 can then execute the stored process steps inorder to execute the loaded computer-executable process steps. Storeddata, e.g., data stored by a storage device, can be accessed by CPU 712during the execution of computer-executable process steps.

Persistent storage, e.g., medium/media 706, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

Network link 728 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 728 mayprovide a connection through local network 724 to a host computer 726 orto equipment operated by a Network or Internet Service Provider (ISP)730. ISP equipment in turn provides data communication services throughthe public, worldwide packet-switching communication network of networksnow commonly referred to as the Internet 732.

A computer called a server host 734 connected to the Internet 732 hostsa process that provides a service in response to information receivedover the Internet 732. For example, server host 734 hosts a process thatprovides information representing video data for presentation at display710. It is contemplated that the components of system 700 can bedeployed in various configurations within other computer systems, e.g.,host and server.

At least some embodiments of the present disclosure are related to theuse of computer system 700 for implementing some or all of thetechniques described herein. According to one embodiment, thosetechniques are performed by computer system 700 in response toprocessing unit 712 executing one or more sequences of one or moreprocessor instructions contained in memory 704. Such instructions, alsocalled computer instructions, software and program code, may be readinto memory 704 from another computer-readable medium 706 such asstorage device or network link. Execution of the sequences ofinstructions contained in memory 704 causes processing unit 712 toperform one or more of the method steps described herein. In alternativeembodiments, hardware, such as ASIC, may be used in place of or incombination with software. Thus, embodiments of the present disclosureare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link and other networks throughcommunications interface, carry information to and from computer system700. Computer system 700 can send and receive information, includingprogram code, through the networks, among others, through network linkand communications interface. In an example using the Internet, a serverhost transmits program code for a particular application, requested by amessage sent from computer, through Internet, ISP equipment, localnetwork and communications interface. The received code may be executedby processor 702 as it is received, or may be stored in memory 704 or instorage device or other non-volatile storage for later execution, orboth.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium for execution by aprocessor. Modules may be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules may be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “subscriber”“consumer” or “customer” should be understood to refer to a user of anapplication or applications as described herein and/or a consumer ofdata supplied by a data provider. By way of example, and not limitation,the term “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

What is claimed is:
 1. A method comprising: receiving, at a computingdevice, a request to create an animated file from a video file, saidanimated file comprising a portion of sequential video frames from thevideo file; parsing, via the computing device, each video frame withinsaid video file, and based on said parsing, identifying pixel data foreach of the video frames; analyzing, via the computing device, the pixeldata for each pair of adjacent frames within the video file, and basedon said analysis, determining discontinuity data, said discontinuitydata representing discontinuities between two pairs of adjacent frames;identifying, via the computing device, based on said discontinuity data,a beginning shot boundary and an ending shot boundary, the beginningshot boundary corresponding to a first pair of adjacent frames withinthe sequence of video frames and the ending shot boundary correspondingto a second pair of adjacent frames within the sequence of video framesthat occurs after the first pair of adjacent frames; analyzing, via thecomputing device, the video frames of the video file based on theidentified shot boundaries, and based on said analysis, determining aset of video frames within said video file that are bookended by thebeginning shot boundary and the ending shot boundary; extracting, viathe computing device, said determined set of video frames; creating, viathe computing device, a new animated file that previously did not existprior to receiving said request, the new animated file comprising thedetermined set of video frames that are extracted from said video file;analyzing, via the computing device, each video frame in said createdanimated file, and based on said analysis, determining a playbacksetting for each video frame, said playback setting comprising a framesampling rate and a time interval between each frame's adjacent frame;and communicating, via the computing device, said created animated fileto a device of a user as a separate file, said communication causing theanimated file to automatically render each video frame at saiddetermined playback setting on said user device.
 2. The method of claim1, wherein said shot boundaries are transition frames within said videofile.
 3. The method of claim 2, wherein said transition frames can beselected from a group consisting of: cut between video frames, fade inbetween frames, fade out between frames, and dissolve effects and wipeeffects.
 4. The method of claim 1, wherein said analysis anddetermination of the discontinuity data comprises the computing deviceexecuting multiple change point detection (MCPD) software on each pairof adjacent frames.
 5. The method of claim 1, wherein said analysis anddetermination of the discontinuity data comprises the computing deviceexecuting frame differencing software on each pair of adjacent frames.6. The method of claim 5, further comprising: determining a length ofthe animated file; comparing said length against a threshold; and whensaid length is greater than said threshold, executing multiple changepoint detection (MCPD) software on the animated file, wherein said MCPDcauses the animated file to be shortened in accordance with saidthreshold.
 7. The method of claim 1, wherein each shot boundarycomprises information indicating a corresponding time index within saidvideo file.
 8. The method of claim 1, further comprising: analyzing saidcreated animated file, and based on said analysis, determining a contextof content of the video frames in said created animated file; causingcommunication, over the network, of said context to a content providerplatform to obtain a digital content item comprising digital contentassociated with said context; receiving, over the network, said digitalcontent item; and causing display said digital content item inassociation with said created animated file.
 9. A non-transitorycomputer-readable storage medium tangibly encoded withcomputer-executable instructions, that when executed by a computingdevice, perform a method comprising: receiving, at the computing device,a request to create an animated file from a video file, said animatedfile comprising a portion of sequential video frames from the videofile; parsing, via the computing device, each video frame within saidvideo file, and based on said parsing, identifying pixel data for eachof the video frames; analyzing, via the computing device, the pixel datafor each pair of adjacent frames within the video file, and based onsaid analysis, determining discontinuity data, said discontinuity datarepresenting discontinuities between two pairs of adjacent frames;identifying, via the computing device, based on said discontinuity data,a beginning shot boundary and an ending shot boundary, the beginningshot boundary corresponding to a first pair of adjacent frames withinthe sequence of video frames and the ending shot boundary correspondingto a second pair of adjacent frames within the sequence of video framesthat occurs after the first pair of adjacent frames; analyzing, via thecomputing device, the video frames of the video file based on theidentified shot boundaries, and based on said analysis, determining aset of video frames within said video file that are bookended by thebeginning shot boundary and the ending shot boundary; extracting, viathe computing device, said determined set of video frames; creating, viathe computing device, a new animated file that previously did not existprior to receiving said request, the new animated file comprising thedetermined set of video frames that are extracted from said video file;analyzing, via the computing device, each video frame in said createdanimated file, and based on said analysis, determining a playbacksetting for each video frame, said playback setting comprising a framesampling rate and a time interval between each frame's adjacent frame;and communicating, via the computing device, said created animated fileto a device of a user as a separate file, said communication causing theanimated file to automatically render each video frame at saiddetermined playback setting on said user device.
 10. The non-transitorycomputer-readable storage medium of claim 9, wherein said shotboundaries are transition frames within said video file.
 11. Thenon-transitory computer-readable storage medium of claim 9, wherein saidanalysis and determination of the discontinuity data comprises thecomputing device executing multiple change point detection (MCPD)software on each pair of adjacent frames.
 12. The non-transitorycomputer-readable storage medium of claim 9, wherein said analysis anddetermination of the discontinuity data comprises the computing deviceexecuting frame differencing software on each pair of adjacent frames.13. The non-transitory computer-readable storage medium of claim 12,further comprising: determining a length of the animated file; comparingsaid length against a threshold; and when said length is greater thansaid threshold, executing multiple change point detection (MCPD)software on the animated file, wherein said MCPD causes the animatedfile to be shortened in accordance with said threshold.
 14. Thenon-transitory computer-readable storage medium of claim 9, furthercomprising: analyzing said created animated file, and based on saidanalysis, determining a context of content of the video frames in saidcreated animated file; causing communication, over the network, of saidcontext to a content provider platform to obtain a digital content itemcomprising digital content associated with said context; receiving, overthe network, said digital content item; and causing display said digitalcontent item in association with said created animated file.
 15. Acomputing device comprising: a processor; and a non-transitorycomputer-readable storage medium for tangibly storing thereon programlogic for execution by the processor, the program logic comprising:logic executed by the processor for receiving, at the computing device,a request to create an animated file from a video file, said animatedfile comprising a portion of sequential video frames from the videofile; logic executed by the processor for parsing, via the computingdevice, each video frame within said video file, and based on saidparsing, identifying pixel data for each of the video frames; logicexecuted by the processor for analyzing, via the computing device, thepixel data for each pair of adjacent frames within the video file, andbased on said analysis, determining discontinuity data, saiddiscontinuity data representing discontinuities between two pairs ofadjacent frames; logic executed by the processor for identifying, viathe computing device, based on said discontinuity data, a beginning shotboundary and an ending shot boundary, the beginning shot boundarycorresponding to a first pair of adjacent frames within the sequence ofvideo frames and the ending shot boundary corresponding to a second pairof adjacent frames within the sequence of video frames that occurs afterthe first pair of adjacent frames; logic executed by the processor foranalyzing, via the computing device, the video frames of the video filebased on the identified shot boundaries, and based on said analysis,determining a set of video frames within said video file that arebookended by the beginning shot boundary and the ending shot boundary;logic executed by the processor for extracting, via the computingdevice, said determined set of video frames; logic executed by theprocessor for creating, via the computing device, a new animated filethat previously did not exist prior to receiving said request, the newanimated file comprising the determined set of video frames that areextracted from said video file; logic executed by the processor foranalyzing, via the computing device, each video frame in said createdanimated file, and based on said analysis, determining a playbacksetting for each video frame, said playback setting comprising a framesampling rate and a time interval between each frame's adjacent frame;and logic executed by the processor for communicating, via the computingdevice, said created animated file to a device of a user as a separatefile, said communication causing the animated file to automaticallyrender each video frame at said determined playback setting on said userdevice.
 16. The computing device of claim 15, further comprising: logicexecuted by the processor for determining a length of the animated file;logic executed by the processor for comparing said length against athreshold; and logic executed by the processor for executing multiplechange point detection (MCPD) software on the animated file when saidlength is greater than said threshold, wherein said MCPD causes theanimated file to be shortened in accordance with said threshold.